000			02863nam a22003733a 4500
001			UPMIN-00005905236
003			UPMIN
005			20230117163131.0
008			221014b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090		0	_aLG 993.5 2011 _bC6 G37
100			_aGarillos, Cinmayii Abarsolo. _91363
245			_aFirefly-simulated annealing (F-SA) algorithm for continuous constrained optimization / _cCinmayii Abarsolo Garillos.
260			_c2011
300			_a112 leaves.
502			_aThesis (BS Computer Science) -- University of the Philippines Mindanao, 2011
520	3		_aIt is certain that NP-hard problems frequently arise and are becoming the major concern of experts in different fields of research and industries. These problems could be in the form of continuous constrained optimization. Due to this impact, several optimization algorithms. in their pure and hybrid forms have been developed and improved to handle this kind of problems. It is in this rationale that the metaheuristic Firefly-Simulated Annealing algorithm was introduced and developed through hybridizing Firefly algorithm (FA) and Simulated Annealing algorithm (SA). The ability of Simulated Annealing algorithm to avoid a firefly from being trapped at a local minimum made it a good candidate as FA's local search. In this study, the researcher employed some parameter settings to F-SA for experimentation and four commonly used cooling schedules for reducing the randomness of FA in F-SA to improve solution quality and convergence. The researcher used some benchmarks functions which have varied characteristics to reflect wide variety of difficulties encountered when solving practical problems, to rigorously test the algorithm performance and to obtain comprehensive results. Based on the overall result of this study, F-SA algorithm, especially F-SA with a cooling schedule, is superior to FA in terms of obtaining high solution quality even in solving constrained optimization problem. However, it is recommended to improve the initialization and solution generation process of F-SA to solve multiobjective optimization problems and more constrained optimization problems with equality constraints.
650	1	7	_aFirefly-simulated Annealing (F-SA) _91364
650	1	7	_aAlgorithm. _91365
650	1	7	_aOptimization. _9733
650	1	7	_aContinuous constrained optimization. _91366
650	1	7	_aFirefly algorithm. _9728
650	1	7	_aHybrid algorithms. _91367
650	1	7	_aMetaheuristics. _91368
650	1	7	_aNon-dterministic polynomial-time hard (NP-hard) _91369
650	1	7	_aSimulated annealing. _91370
650	1	7	_aFirefly algorithm (FA) _91371
650	1	7	_aNP-hard problems. _91372
658			_aUndergraduate Thesis _cCMSC200, _2BSCS
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c2682 _d2682